PARSIMONIOUS DYNAMICAL RECONSTRUCTION

Many nonlinear deterministic models of time series have large numbers of parameters and tend to overfit in the presence of noise. This paper shows how to generate radial basis function models with small numbers of parameters for a given quality of fit. It also addresses questions of how to select subsets from candidate sets of centers for radial basis function models, and what kinds of basis functions to use, as well as how large a model is appropriate.